3,831 research outputs found
Learning to Reason: End-to-End Module Networks for Visual Question Answering
Natural language questions are inherently compositional, and many are most
easily answered by reasoning about their decomposition into modular
sub-problems. For example, to answer "is there an equal number of balls and
boxes?" we can look for balls, look for boxes, count them, and compare the
results. The recently proposed Neural Module Network (NMN) architecture
implements this approach to question answering by parsing questions into
linguistic substructures and assembling question-specific deep networks from
smaller modules that each solve one subtask. However, existing NMN
implementations rely on brittle off-the-shelf parsers, and are restricted to
the module configurations proposed by these parsers rather than learning them
from data. In this paper, we propose End-to-End Module Networks (N2NMNs), which
learn to reason by directly predicting instance-specific network layouts
without the aid of a parser. Our model learns to generate network structures
(by imitating expert demonstrations) while simultaneously learning network
parameters (using the downstream task loss). Experimental results on the new
CLEVR dataset targeted at compositional question answering show that N2NMNs
achieve an error reduction of nearly 50% relative to state-of-the-art
attentional approaches, while discovering interpretable network architectures
specialized for each question
Visual Question Answering: A Survey of Methods and Datasets
Visual Question Answering (VQA) is a challenging task that has received
increasing attention from both the computer vision and the natural language
processing communities. Given an image and a question in natural language, it
requires reasoning over visual elements of the image and general knowledge to
infer the correct answer. In the first part of this survey, we examine the
state of the art by comparing modern approaches to the problem. We classify
methods by their mechanism to connect the visual and textual modalities. In
particular, we examine the common approach of combining convolutional and
recurrent neural networks to map images and questions to a common feature
space. We also discuss memory-augmented and modular architectures that
interface with structured knowledge bases. In the second part of this survey,
we review the datasets available for training and evaluating VQA systems. The
various datatsets contain questions at different levels of complexity, which
require different capabilities and types of reasoning. We examine in depth the
question/answer pairs from the Visual Genome project, and evaluate the
relevance of the structured annotations of images with scene graphs for VQA.
Finally, we discuss promising future directions for the field, in particular
the connection to structured knowledge bases and the use of natural language
processing models.Comment: 25 page
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